Wearable vision detection of environmental fall risk using Gabor Barcodes

In this paper, a method todetect environmental hazards related to a fall risk using a mobile vision system is proposed. First-person perspective videos are proposed to provide objective evidence on cause and circumstances of perturbedbalance during activities of daily living, targeted to seniors. A classification problem was defined with 17 total classes of potential fall risks, including slope changes (e.g., stairs, curbs, ramps) and surfaces (e.g., gravel, grass, concrete). Data was collected using chest-mounted GoPro camera. Gabor Barcodes were applied for texture-based feature extraction and used Hamming distance for finding the members of the class. Initial results, with a mean accuracy of 88.5%, are promising.